Overview

Dataset statistics

Number of variables40
Number of observations8030
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 MiB
Average record size in memory320.0 B

Variable types

Numeric9
Categorical31

Alerts

Managed By_Ethiopia Field Office has constant value "0.0" Constant
Managed By_Haiti Field Office has constant value "0.0" Constant
Product Group_ACT has constant value "0.0" Constant
Sub Classification_ACT has constant value "0.0" Constant
Unit of Measure (Per Pack) is highly correlated with Unit PriceHigh correlation
Line Item Quantity is highly correlated with Line Item Value and 3 other fieldsHigh correlation
Line Item Value is highly correlated with Line Item Quantity and 3 other fieldsHigh correlation
Pack Price is highly correlated with Unit PriceHigh correlation
Unit Price is highly correlated with Unit of Measure (Per Pack) and 1 other fieldsHigh correlation
Weight (Kilograms) is highly correlated with Line Item Quantity and 3 other fieldsHigh correlation
Freight Cost (USD) is highly correlated with Line Item Quantity and 2 other fieldsHigh correlation
Line Item Insurance (USD) is highly correlated with Line Item Quantity and 2 other fieldsHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Fulfill Via_Direct Drop is highly correlated with Fulfill Via_From RDC and 3 other fieldsHigh correlation
Fulfill Via_From RDC is highly correlated with Fulfill Via_Direct Drop and 3 other fieldsHigh correlation
Vendor INCO Term_DDP is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Vendor INCO Term_EXW is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Fulfill Via_Direct Drop and 3 other fieldsHigh correlation
Shipment Mode_Air is highly correlated with Shipment Mode_TruckHigh correlation
Shipment Mode_Truck is highly correlated with Shipment Mode_AirHigh correlation
Product Group_ANTM is highly correlated with Sub Classification_MalariaHigh correlation
Product Group_ARV is highly correlated with Product Group_HRDT and 1 other fieldsHigh correlation
Product Group_HRDT is highly correlated with Product Group_ARV and 1 other fieldsHigh correlation
Product Group_MRDT is highly correlated with Sub Classification_MalariaHigh correlation
Sub Classification_Adult is highly correlated with Sub Classification_PediatricHigh correlation
Sub Classification_HIV test is highly correlated with Product Group_ARV and 1 other fieldsHigh correlation
Sub Classification_Malaria is highly correlated with Product Group_ANTM and 1 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Sub Classification_AdultHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
Unit of Measure (Per Pack) is highly correlated with Unit Price and 1 other fieldsHigh correlation
Line Item Quantity is highly correlated with Line Item Value and 3 other fieldsHigh correlation
Line Item Value is highly correlated with Line Item Quantity and 2 other fieldsHigh correlation
Pack Price is highly correlated with Unit PriceHigh correlation
Unit Price is highly correlated with Unit of Measure (Per Pack) and 1 other fieldsHigh correlation
Weight (Kilograms) is highly correlated with Line Item Quantity and 3 other fieldsHigh correlation
Freight Cost (USD) is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Line Item Insurance (USD) is highly correlated with Line Item Quantity and 2 other fieldsHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Fulfill Via_Direct Drop is highly correlated with Fulfill Via_From RDC and 3 other fieldsHigh correlation
Fulfill Via_From RDC is highly correlated with Fulfill Via_Direct Drop and 3 other fieldsHigh correlation
Vendor INCO Term_DDP is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Vendor INCO Term_EXW is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Fulfill Via_Direct Drop and 3 other fieldsHigh correlation
Shipment Mode_Air is highly correlated with Shipment Mode_TruckHigh correlation
Shipment Mode_Truck is highly correlated with Shipment Mode_AirHigh correlation
Product Group_ANTM is highly correlated with Sub Classification_MalariaHigh correlation
Product Group_ARV is highly correlated with Product Group_HRDT and 1 other fieldsHigh correlation
Product Group_HRDT is highly correlated with Product Group_ARV and 1 other fieldsHigh correlation
Product Group_MRDT is highly correlated with Sub Classification_MalariaHigh correlation
Sub Classification_Adult is highly correlated with Sub Classification_PediatricHigh correlation
Sub Classification_HIV test is highly correlated with Product Group_ARV and 1 other fieldsHigh correlation
Sub Classification_Malaria is highly correlated with Product Group_ANTM and 1 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Unit of Measure (Per Pack) and 1 other fieldsHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
Line Item Quantity is highly correlated with Line Item Value and 2 other fieldsHigh correlation
Line Item Value is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Pack Price is highly correlated with Unit PriceHigh correlation
Unit Price is highly correlated with Pack PriceHigh correlation
Weight (Kilograms) is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Freight Cost (USD) is highly correlated with Weight (Kilograms)High correlation
Line Item Insurance (USD) is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Fulfill Via_Direct Drop is highly correlated with Fulfill Via_From RDC and 3 other fieldsHigh correlation
Fulfill Via_From RDC is highly correlated with Fulfill Via_Direct Drop and 3 other fieldsHigh correlation
Vendor INCO Term_DDP is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Vendor INCO Term_EXW is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Fulfill Via_Direct Drop and 3 other fieldsHigh correlation
Shipment Mode_Air is highly correlated with Shipment Mode_TruckHigh correlation
Shipment Mode_Truck is highly correlated with Shipment Mode_AirHigh correlation
Product Group_ANTM is highly correlated with Sub Classification_MalariaHigh correlation
Product Group_ARV is highly correlated with Product Group_HRDT and 1 other fieldsHigh correlation
Product Group_HRDT is highly correlated with Product Group_ARV and 1 other fieldsHigh correlation
Product Group_MRDT is highly correlated with Sub Classification_MalariaHigh correlation
Sub Classification_Adult is highly correlated with Sub Classification_PediatricHigh correlation
Sub Classification_HIV test is highly correlated with Product Group_ARV and 1 other fieldsHigh correlation
Sub Classification_Malaria is highly correlated with Product Group_ANTM and 1 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Sub Classification_AdultHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
Vendor INCO Term_CIF is highly correlated with Sub Classification_ACT and 3 other fieldsHigh correlation
Vendor INCO Term_FCA is highly correlated with Sub Classification_ACT and 3 other fieldsHigh correlation
Vendor INCO Term_DAP is highly correlated with Sub Classification_ACT and 3 other fieldsHigh correlation
Managed By_South Africa Field Office is highly correlated with Sub Classification_ACT and 4 other fieldsHigh correlation
Sub Classification_Malaria is highly correlated with Sub Classification_ACT and 5 other fieldsHigh correlation
Shipment Mode_Truck is highly correlated with Sub Classification_ACT and 4 other fieldsHigh correlation
Sub Classification_ACT is highly correlated with Vendor INCO Term_CIF and 29 other fieldsHigh correlation
Vendor INCO Term_DDP is highly correlated with Sub Classification_ACT and 6 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Sub Classification_ACT and 4 other fieldsHigh correlation
Vendor INCO Term_CIP is highly correlated with Sub Classification_ACT and 3 other fieldsHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field Office and 4 other fieldsHigh correlation
Product Group_ARV is highly correlated with Sub Classification_ACT and 5 other fieldsHigh correlation
Shipment Mode_Ocean is highly correlated with Sub Classification_ACT and 3 other fieldsHigh correlation
Managed By_Ethiopia Field Office is highly correlated with Vendor INCO Term_CIF and 29 other fieldsHigh correlation
Product Group_ACT is highly correlated with Vendor INCO Term_CIF and 29 other fieldsHigh correlation
Sub Classification_HIV test - Ancillary is highly correlated with Sub Classification_ACT and 3 other fieldsHigh correlation
Product Group_HRDT is highly correlated with Sub Classification_ACT and 5 other fieldsHigh correlation
First Line Designation_Yes is highly correlated with Sub Classification_ACT and 4 other fieldsHigh correlation
Vendor INCO Term_EXW is highly correlated with Sub Classification_ACT and 6 other fieldsHigh correlation
Product Group_ANTM is highly correlated with Sub Classification_Malaria and 4 other fieldsHigh correlation
Fulfill Via_From RDC is highly correlated with Sub Classification_ACT and 7 other fieldsHigh correlation
Managed By_Haiti Field Office is highly correlated with Vendor INCO Term_CIF and 29 other fieldsHigh correlation
Shipment Mode_Air is highly correlated with Shipment Mode_Truck and 4 other fieldsHigh correlation
Fulfill Via_Direct Drop is highly correlated with Sub Classification_ACT and 7 other fieldsHigh correlation
Shipment Mode_Air Charter is highly correlated with Sub Classification_ACT and 3 other fieldsHigh correlation
Sub Classification_HIV test is highly correlated with Sub Classification_ACT and 5 other fieldsHigh correlation
First Line Designation_No is highly correlated with Sub Classification_ACT and 4 other fieldsHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Sub Classification_ACT and 7 other fieldsHigh correlation
Product Group_MRDT is highly correlated with Sub Classification_Malaria and 4 other fieldsHigh correlation
Sub Classification_Adult is highly correlated with Sub Classification_ACT and 4 other fieldsHigh correlation
Vendor INCO Term_DDU is highly correlated with Sub Classification_ACT and 3 other fieldsHigh correlation
df_index is highly correlated with Fulfill Via_Direct Drop and 4 other fieldsHigh correlation
Unit of Measure (Per Pack) is highly correlated with Pack Price and 3 other fieldsHigh correlation
Line Item Quantity is highly correlated with Line Item Value and 4 other fieldsHigh correlation
Line Item Value is highly correlated with Line Item Quantity and 3 other fieldsHigh correlation
Pack Price is highly correlated with Unit of Measure (Per Pack) and 1 other fieldsHigh correlation
Unit Price is highly correlated with Unit of Measure (Per Pack) and 6 other fieldsHigh correlation
Weight (Kilograms) is highly correlated with Line Item Quantity and 3 other fieldsHigh correlation
Freight Cost (USD) is highly correlated with Line Item Quantity and 3 other fieldsHigh correlation
Line Item Insurance (USD) is highly correlated with Line Item Quantity and 3 other fieldsHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Fulfill Via_Direct Drop is highly correlated with df_index and 4 other fieldsHigh correlation
Fulfill Via_From RDC is highly correlated with df_index and 4 other fieldsHigh correlation
Vendor INCO Term_DDP is highly correlated with df_index and 4 other fieldsHigh correlation
Vendor INCO Term_EXW is highly correlated with df_index and 3 other fieldsHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with df_index and 4 other fieldsHigh correlation
Shipment Mode_Air is highly correlated with Shipment Mode_TruckHigh correlation
Shipment Mode_Truck is highly correlated with Shipment Mode_AirHigh correlation
Product Group_ANTM is highly correlated with Sub Classification_MalariaHigh correlation
Product Group_ARV is highly correlated with Unit Price and 3 other fieldsHigh correlation
Product Group_HRDT is highly correlated with Unit Price and 3 other fieldsHigh correlation
Product Group_MRDT is highly correlated with Sub Classification_MalariaHigh correlation
Sub Classification_Adult is highly correlated with Unit of Measure (Per Pack) and 2 other fieldsHigh correlation
Sub Classification_HIV test is highly correlated with Unit Price and 2 other fieldsHigh correlation
Sub Classification_HIV test - Ancillary is highly correlated with Product Group_ARV and 1 other fieldsHigh correlation
Sub Classification_Malaria is highly correlated with Product Group_ANTM and 1 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Unit of Measure (Per Pack) and 2 other fieldsHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
df_index has unique values Unique

Reproduction

Analysis started2022-05-21 15:46:03.459696
Analysis finished2022-05-21 15:46:25.305526
Duration21.85 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct8030
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5589.931756
Minimum0
Maximum10323
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size62.9 KiB
2022-05-21T21:16:25.414236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile711.9
Q12729.25
median6002.5
Q38273.75
95-th percentile9914.55
Maximum10323
Range10323
Interquartile range (IQR)5544.5

Descriptive statistics

Standard deviation3046.016925
Coefficient of variation (CV)0.5449112901
Kurtosis-1.286295953
Mean5589.931756
Median Absolute Deviation (MAD)2671
Skewness-0.1820781911
Sum44887152
Variance9278219.107
MonotonicityStrictly increasing
2022-05-21T21:16:25.556854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
75891
 
< 0.1%
76021
 
< 0.1%
76011
 
< 0.1%
76001
 
< 0.1%
75991
 
< 0.1%
75981
 
< 0.1%
75971
 
< 0.1%
75961
 
< 0.1%
75951
 
< 0.1%
Other values (8020)8020
99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
103231
< 0.1%
103221
< 0.1%
103211
< 0.1%
103201
< 0.1%
103191
< 0.1%
103181
< 0.1%
103171
< 0.1%
103161
< 0.1%
103151
< 0.1%
103141
< 0.1%

Unit of Measure (Per Pack)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.051315809
Minimum3.218875825
Maximum5.598421959
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.9 KiB
2022-05-21T21:16:25.671547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.218875825
5-th percentile3.401197382
Q13.401197382
median4.094344562
Q34.094344562
95-th percentile5.480638923
Maximum5.598421959
Range2.379546134
Interquartile range (IQR)0.6931471806

Descriptive statistics

Standard deviation0.6047184472
Coefficient of variation (CV)0.149264702
Kurtosis0.362615847
Mean4.051315809
Median Absolute Deviation (MAD)0.4054651081
Skewness0.8896652381
Sum32532.06595
Variance0.3656844003
MonotonicityNot monotonic
2022-05-21T21:16:25.769285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
4.0943445623937
49.0%
3.4011973822502
31.2%
5.480638923636
 
7.9%
4.787491743398
 
5.0%
4.49980967199
 
2.5%
4.60517018698
 
1.2%
3.21887582592
 
1.1%
5.19295685174
 
0.9%
5.29831736769
 
0.9%
4.43081679917
 
0.2%
Other values (3)8
 
0.1%
ValueCountFrequency (%)
3.21887582592
 
1.1%
3.4011973822502
31.2%
4.0943445623937
49.0%
4.43081679917
 
0.2%
4.49980967199
 
2.5%
4.60517018698
 
1.2%
4.787491743398
 
5.0%
4.96981331
 
< 0.1%
5.0751738151
 
< 0.1%
5.19295685174
 
0.9%
ValueCountFrequency (%)
5.5984219596
 
0.1%
5.480638923636
7.9%
5.29831736769
 
0.9%
5.19295685174
 
0.9%
5.0751738151
 
< 0.1%
4.96981331
 
< 0.1%
4.787491743398
5.0%
4.60517018698
 
1.2%
4.49980967199
 
2.5%
4.43081679917
 
0.2%

Line Item Quantity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4614
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.345260544
Minimum2.397895273
Maximum13.3061938
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.9 KiB
2022-05-21T21:16:25.894949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.397895273
5-th percentile4.127134385
Q16.845081578
median8.616585833
Q310.14844149
95-th percentile11.51292546
Maximum13.3061938
Range10.90829852
Interquartile range (IQR)3.303359908

Descriptive statistics

Standard deviation2.266866612
Coefficient of variation (CV)0.2716352114
Kurtosis-0.4994622707
Mean8.345260544
Median Absolute Deviation (MAD)1.663229737
Skewness-0.4526597657
Sum67012.44217
Variance5.138684237
MonotonicityNot monotonic
2022-05-21T21:16:26.032581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.21034037288
 
1.1%
6.90775527964
 
0.8%
9.90348755363
 
0.8%
10.8197782860
 
0.7%
4.60517018656
 
0.7%
7.6009024653
 
0.7%
8.51719319152
 
0.6%
8.00636756852
 
0.6%
8.2940496450
 
0.6%
11.5129254649
 
0.6%
Other values (4604)7443
92.7%
ValueCountFrequency (%)
2.39789527315
0.2%
2.4849066516
0.2%
2.5649493579
 
0.1%
2.639057339
 
0.1%
2.7080502017
 
0.1%
2.77258872211
0.1%
2.8332133448
 
0.1%
2.8903717588
 
0.1%
2.94443897915
0.2%
2.99573227425
0.3%
ValueCountFrequency (%)
13.30619381
 
< 0.1%
13.227078281
 
< 0.1%
13.151922183
< 0.1%
13.151001371
 
< 0.1%
13.039070891
 
< 0.1%
12.994530011
 
< 0.1%
12.904110351
 
< 0.1%
12.899219832
< 0.1%
12.793859311
 
< 0.1%
12.785143661
 
< 0.1%

Line Item Value
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7252
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.3121574
Minimum4.521571162
Maximum15.56795689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.9 KiB
2022-05-21T21:16:26.174201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4.521571162
5-th percentile6.317400115
Q18.707511025
median10.49266214
Q312.10065612
95-th percentile13.56684241
Maximum15.56795689
Range11.04638573
Interquartile range (IQR)3.393145099

Descriptive statistics

Standard deviation2.225946177
Coefficient of variation (CV)0.2158564974
Kurtosis-0.6986059651
Mean10.3121574
Median Absolute Deviation (MAD)1.680399218
Skewness-0.2933036894
Sum82806.6239
Variance4.954836381
MonotonicityNot monotonic
2022-05-21T21:16:26.497337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.2060726525
 
0.3%
9.68034400111
 
0.1%
8.07090608910
 
0.1%
13.305517929
 
0.1%
10.047639849
 
0.1%
12.526618738
 
0.1%
10.779789287
 
0.1%
7.9373746967
 
0.1%
12.087289117
 
0.1%
6.7334018926
 
0.1%
Other values (7242)7931
98.8%
ValueCountFrequency (%)
4.5215711621
< 0.1%
4.6959245491
< 0.1%
4.6986605291
< 0.1%
4.7116900291
< 0.1%
4.7494436751
< 0.1%
4.7660127111
< 0.1%
4.800736971
< 0.1%
4.8040210451
< 0.1%
4.8170505451
< 0.1%
4.8194747891
< 0.1%
ValueCountFrequency (%)
15.567956891
< 0.1%
15.488841381
< 0.1%
15.452585961
< 0.1%
15.416763461
< 0.1%
15.257388561
< 0.1%
15.205298811
< 0.1%
15.184882541
< 0.1%
15.177512232
< 0.1%
15.150498471
< 0.1%
15.092949691
< 0.1%

Pack Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct980
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.966913459
Minimum0.08157998699
Maximum4.24563401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.9 KiB
2022-05-21T21:16:26.642947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.08157998699
5-th percentile0.6418538862
Q11.235458844
median2.014903021
Q32.519308077
95-th percentile3.506457892
Maximum4.24563401
Range4.164054023
Interquartile range (IQR)1.283849232

Descriptive statistics

Standard deviation0.8877803672
Coefficient of variation (CV)0.4513571062
Kurtosis-0.6221087941
Mean1.966913459
Median Absolute Deviation (MAD)0.6411874416
Skewness0.1424767998
Sum15794.31508
Variance0.7881539804
MonotonicityNot monotonic
2022-05-21T21:16:26.769608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.4176979139
 
1.7%
2.995732274109
 
1.4%
0.667829372691
 
1.1%
2.17019590591
 
1.1%
0.741937344789
 
1.1%
0.891998039387
 
1.1%
0.815364813387
 
1.1%
2.11142458881
 
1.0%
2.04381436476
 
0.9%
1.94591014974
 
0.9%
Other values (970)7106
88.5%
ValueCountFrequency (%)
0.081579986994
 
< 0.1%
0.09531017982
 
< 0.1%
0.13102826242
 
< 0.1%
0.15700374881
 
< 0.1%
0.18232155681
 
< 0.1%
0.223143551324
0.3%
0.262364264521
0.3%
0.27763173665
 
0.1%
0.2926696143
 
< 0.1%
0.30010459253
 
< 0.1%
ValueCountFrequency (%)
4.245634011
 
< 0.1%
4.2443437793
 
< 0.1%
4.2339615674
 
< 0.1%
4.2242025492
 
< 0.1%
4.2046926192
 
< 0.1%
4.2009542971
 
< 0.1%
4.19870457815
0.2%
4.1858596713
 
< 0.1%
4.1847944651
 
< 0.1%
4.1779194861
 
< 0.1%

Unit Price
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct127
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.071966746
Minimum-4.605170186
Maximum0.5306282511
Zeros1
Zeros (%)< 0.1%
Negative7959
Negative (%)99.1%
Memory size62.9 KiB
2022-05-21T21:16:26.900259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-4.605170186
5-th percentile-4.605170186
Q1-2.813410717
median-1.966112856
Q3-1.30933332
95-th percentile-0.2876820725
Maximum0.5306282511
Range5.135798437
Interquartile range (IQR)1.504077397

Descriptive statistics

Standard deviation1.099054047
Coefficient of variation (CV)-0.5304400028
Kurtosis-0.04970734499
Mean-2.071966746
Median Absolute Deviation (MAD)0.7282385004
Skewness-0.4005709989
Sum-16637.89297
Variance1.207919798
MonotonicityNot monotonic
2022-05-21T21:16:27.033901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.218875825690
 
8.6%
-4.605170186482
 
6.0%
-2.120263536449
 
5.6%
-1.966112856412
 
5.1%
-2.207274913358
 
4.5%
-1.832581464337
 
4.2%
-2.995732274332
 
4.1%
-1.660731207315
 
3.9%
-2.040220829304
 
3.8%
-1.897119985274
 
3.4%
Other values (117)4077
50.8%
ValueCountFrequency (%)
-4.605170186482
6.0%
-3.912023005118
 
1.5%
-3.506557897229
 
2.9%
-3.218875825690
8.6%
-2.995732274332
4.1%
-2.813410717266
 
3.3%
-2.659260037222
 
2.8%
-2.525728644113
 
1.4%
-2.407945609148
 
1.8%
-2.302585093187
 
2.3%
ValueCountFrequency (%)
0.53062825111
 
< 0.1%
0.518793793415
0.2%
0.47000362921
 
< 0.1%
0.46373401621
 
< 0.1%
0.44468582132
 
< 0.1%
0.41210965081
 
< 0.1%
0.40546510815
 
0.1%
0.34358970441
 
< 0.1%
0.31481073981
 
< 0.1%
0.27763173661
 
< 0.1%

Weight (Kilograms)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3544
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.08486449
Minimum1.504077397
Maximum11.38724886
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.9 KiB
2022-05-21T21:16:27.170535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.504077397
5-th percentile3.518212227
Q15.749392986
median7.388018549
Q38.56712556
95-th percentile9.865419548
Maximum11.38724886
Range9.88317146
Interquartile range (IQR)2.817732574

Descriptive statistics

Standard deviation1.95982352
Coefficient of variation (CV)0.2766211722
Kurtosis-0.4164495317
Mean7.08486449
Median Absolute Deviation (MAD)1.363455938
Skewness-0.449578562
Sum56891.46185
Variance3.840908229
MonotonicityNot monotonic
2022-05-21T21:16:27.306173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.82864139628
 
0.3%
2.19722457722
 
0.3%
4.3307333418
 
0.2%
4.71849887118
 
0.2%
3.40119738217
 
0.2%
6.04500531417
 
0.2%
1.79175946916
 
0.2%
3.68887945415
 
0.2%
4.78749174315
 
0.2%
3.58351893814
 
0.2%
Other values (3534)7850
97.8%
ValueCountFrequency (%)
1.5040773972
 
< 0.1%
1.60943791211
0.1%
1.7047480922
 
< 0.1%
1.79175946916
0.2%
1.8718021772
 
< 0.1%
1.9459101499
0.1%
2.0149030215
 
0.1%
2.07944154211
0.1%
2.1400661631
 
< 0.1%
2.19722457722
0.3%
ValueCountFrequency (%)
11.387248861
 
< 0.1%
11.374536582
 
< 0.1%
11.351911291
 
< 0.1%
11.316143863
 
< 0.1%
11.312472524
< 0.1%
11.257129581
 
< 0.1%
11.229022913
 
< 0.1%
11.19787223
 
< 0.1%
11.157806459
0.1%
11.141151392
 
< 0.1%

Freight Cost (USD)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4994
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.72762346
Minimum2.664446564
Maximum12.17882189
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.9 KiB
2022-05-21T21:16:27.444802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.664446564
5-th percentile6.709567953
Q17.910472816
median8.802870369
Q39.596747016
95-th percentile10.65349227
Maximum12.17882189
Range9.51437533
Interquartile range (IQR)1.6862742

Descriptive statistics

Standard deviation1.228565381
Coefficient of variation (CV)0.1407674594
Kurtosis0.2529671375
Mean8.72762346
Median Absolute Deviation (MAD)0.8327102866
Skewness-0.2802643972
Sum70082.81638
Variance1.509372895
MonotonicityNot monotonic
2022-05-21T21:16:27.575452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.18359590637
 
0.5%
8.72374871927
 
0.3%
8.91540539426
 
0.3%
8.89970760219
 
0.2%
9.14222104719
 
0.2%
9.50286519918
 
0.2%
9.74624877616
 
0.2%
9.7130834215
 
0.2%
9.92814748215
 
0.2%
8.61796298614
 
0.2%
Other values (4984)7824
97.4%
ValueCountFrequency (%)
2.6644465642
< 0.1%
2.8746939451
 
< 0.1%
3.1041381471
 
< 0.1%
3.3745111163
< 0.1%
3.4011973821
 
< 0.1%
3.4173987611
 
< 0.1%
3.7459684211
 
< 0.1%
3.8712010111
 
< 0.1%
4.1442452211
 
< 0.1%
4.4543472961
 
< 0.1%
ValueCountFrequency (%)
12.178821892
 
< 0.1%
11.9935776313
0.2%
11.897171431
 
< 0.1%
11.89638251
 
< 0.1%
11.797279034
 
< 0.1%
11.785078661
 
< 0.1%
11.774083163
 
< 0.1%
11.754187721
 
< 0.1%
11.73086221
 
< 0.1%
11.72759324
 
< 0.1%

Line Item Insurance (USD)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5927
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.819218077
Minimum-1.897119985
Maximum8.950071111
Zeros2
Zeros (%)< 0.1%
Negative507
Negative (%)6.3%
Memory size62.9 KiB
2022-05-21T21:16:27.709095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1.897119985
5-th percentile-0.2357223335
Q12.199721342
median4.036803718
Q35.588885891
95-th percentile7.091837365
Maximum8.950071111
Range10.8471911
Interquartile range (IQR)3.389164548

Descriptive statistics

Standard deviation2.246860513
Coefficient of variation (CV)0.5883038013
Kurtosis-0.6549805057
Mean3.819218077
Median Absolute Deviation (MAD)1.672911108
Skewness-0.3284660099
Sum30668.32116
Variance5.048382164
MonotonicityNot monotonic
2022-05-21T21:16:27.845729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.27296567613
 
0.2%
5.79909265413
 
0.2%
-0.867500567713
 
0.2%
-1.38629436113
 
0.2%
-1.07880966112
 
0.1%
1.63315443911
 
0.1%
-0.616186139411
 
0.1%
-0.820980552111
 
0.1%
-0.0408219945211
 
0.1%
-1.23787435610
 
0.1%
Other values (5917)7912
98.5%
ValueCountFrequency (%)
-1.8971199854
< 0.1%
-1.8325814642
 
< 0.1%
-1.7719568423
 
< 0.1%
-1.7147984286
0.1%
-1.6607312075
0.1%
-1.6094379124
< 0.1%
-1.5606477482
 
< 0.1%
-1.5141277336
0.1%
-1.469675976
0.1%
-1.4271163569
0.1%
ValueCountFrequency (%)
8.9500711111
< 0.1%
8.6878165911
< 0.1%
8.6257444111
< 0.1%
8.6087016081
< 0.1%
8.5724462361
< 0.1%
8.5623214211
< 0.1%
8.5491355581
< 0.1%
8.5457806482
< 0.1%
8.53662321
< 0.1%
8.5258972011
< 0.1%

Managed By_Ethiopia Field Office
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
8030 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08030
100.0%

Length

2022-05-21T21:16:27.968401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:28.036219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08030
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Managed By_Haiti Field Office
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
8030 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08030
100.0%

Length

2022-05-21T21:16:28.097057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:28.164875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08030
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Managed By_PMO - US
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
1.0
7982 
0.0
 
48

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.07982
99.4%
0.048
 
0.6%

Length

2022-05-21T21:16:28.225712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:28.294528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.07982
99.4%
0.048
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Managed By_South Africa Field Office
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
7982 
1.0
 
48

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07982
99.4%
1.048
 
0.6%

Length

2022-05-21T21:16:28.361349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:28.431165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.07982
99.4%
1.048
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Fulfill Via_Direct Drop
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
5070 
1.0
2960 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.05070
63.1%
1.02960
36.9%

Length

2022-05-21T21:16:28.499980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:28.568796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.05070
63.1%
1.02960
36.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Fulfill Via_From RDC
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
1.0
5070 
0.0
2960 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.05070
63.1%
0.02960
36.9%

Length

2022-05-21T21:16:28.638609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:28.708422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.05070
63.1%
0.02960
36.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vendor INCO Term_CIF
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
8029 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08029
> 99.9%
1.01
 
< 0.1%

Length

2022-05-21T21:16:28.778236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:28.847051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08029
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vendor INCO Term_CIP
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
7795 
1.0
 
235

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07795
97.1%
1.0235
 
2.9%

Length

2022-05-21T21:16:28.914868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:28.982690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.07795
97.1%
1.0235
 
2.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vendor INCO Term_DAP
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
8024 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08024
99.9%
1.06
 
0.1%

Length

2022-05-21T21:16:29.050505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:29.118324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08024
99.9%
1.06
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vendor INCO Term_DDP
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
6918 
1.0
1112 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06918
86.2%
1.01112
 
13.8%

Length

2022-05-21T21:16:29.185145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:29.253961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.06918
86.2%
1.01112
 
13.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vendor INCO Term_DDU
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
8024 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08024
99.9%
1.06
 
0.1%

Length

2022-05-21T21:16:29.322779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:29.391594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08024
99.9%
1.06
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vendor INCO Term_EXW
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
6646 
1.0
1384 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.06646
82.8%
1.01384
 
17.2%

Length

2022-05-21T21:16:29.458456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:29.526270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.06646
82.8%
1.01384
 
17.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vendor INCO Term_FCA
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
7814 
1.0
 
216

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07814
97.3%
1.0216
 
2.7%

Length

2022-05-21T21:16:29.593097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:29.659914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.07814
97.3%
1.0216
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vendor INCO Term_N/A - From RDC
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
1.0
5070 
0.0
2960 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.05070
63.1%
0.02960
36.9%

Length

2022-05-21T21:16:29.726734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:29.793557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.05070
63.1%
0.02960
36.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Shipment Mode_Air
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
1.0
4262 
0.0
3768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.04262
53.1%
0.03768
46.9%

Length

2022-05-21T21:16:29.861373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:29.929194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.04262
53.1%
0.03768
46.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Shipment Mode_Air Charter
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
7395 
1.0
 
635

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07395
92.1%
1.0635
 
7.9%

Length

2022-05-21T21:16:29.996013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:30.064791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.07395
92.1%
1.0635
 
7.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Shipment Mode_Ocean
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
7663 
1.0
 
367

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07663
95.4%
1.0367
 
4.6%

Length

2022-05-21T21:16:30.131612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:30.437793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.07663
95.4%
1.0367
 
4.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Shipment Mode_Truck
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
5548 
1.0
2482 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.05548
69.1%
1.02482
30.9%

Length

2022-05-21T21:16:30.517579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:30.585398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.05548
69.1%
1.02482
30.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_ACT
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
8030 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08030
100.0%

Length

2022-05-21T21:16:30.654214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:30.721035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08030
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_ANTM
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
8025 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08025
99.9%
1.05
 
0.1%

Length

2022-05-21T21:16:30.781872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:30.850688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08025
99.9%
1.05
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_ARV
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
1.0
7842 
0.0
 
188

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.07842
97.7%
0.0188
 
2.3%

Length

2022-05-21T21:16:30.917509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:30.985328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.07842
97.7%
0.0188
 
2.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_HRDT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
7853 
1.0
 
177

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07853
97.8%
1.0177
 
2.2%

Length

2022-05-21T21:16:31.053147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:31.120965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.07853
97.8%
1.0177
 
2.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_MRDT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
8024 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08024
99.9%
1.06
 
0.1%

Length

2022-05-21T21:16:31.188783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:31.256602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08024
99.9%
1.06
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_ACT
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
8030 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08030
100.0%

Length

2022-05-21T21:16:31.324464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:31.390285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08030
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_Adult
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
1.0
6169 
0.0
1861 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.06169
76.8%
0.01861
 
23.2%

Length

2022-05-21T21:16:31.451122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:31.518940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.06169
76.8%
0.01861
 
23.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_HIV test
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
7881 
1.0
 
149

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07881
98.1%
1.0149
 
1.9%

Length

2022-05-21T21:16:31.586757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:31.653578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.07881
98.1%
1.0149
 
1.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_HIV test - Ancillary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
8002 
1.0
 
28

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08002
99.7%
1.028
 
0.3%

Length

2022-05-21T21:16:31.720402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:31.788220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08002
99.7%
1.028
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_Malaria
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
8019 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08019
99.9%
1.011
 
0.1%

Length

2022-05-21T21:16:31.854044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:31.921861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08019
99.9%
1.011
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_Pediatric
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
6357 
1.0
1673 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.06357
79.2%
1.01673
 
20.8%

Length

2022-05-21T21:16:31.988681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:32.055503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.06357
79.2%
1.01673
 
20.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

First Line Designation_No
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
0.0
5245 
1.0
2785 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.05245
65.3%
1.02785
34.7%

Length

2022-05-21T21:16:32.123323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:32.191142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.05245
65.3%
1.02785
34.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

First Line Designation_Yes
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
1.0
5245 
0.0
2785 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.05245
65.3%
0.02785
34.7%

Length

2022-05-21T21:16:32.257961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T21:16:32.326776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.05245
65.3%
0.02785
34.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-05-21T21:16:22.453159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:12.670293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:13.851167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:15.102819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:16.907982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:18.055921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:19.145967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:20.214107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:21.420923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:22.567852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:12.823917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:14.021712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:15.225455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:17.030625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:18.170609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:19.266680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:20.356725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:21.534618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:22.692519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:12.955570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:14.168322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:15.352150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:17.194187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:18.292250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:19.381380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:20.470464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:21.651305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:22.816188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:13.073252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:14.320875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:15.489748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:17.311873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:18.442846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:19.496028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:20.724785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:21.767992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:22.938859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:13.198916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:14.451526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:15.621433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:17.434586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:18.559543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:19.615746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:20.862415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:21.887672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:23.051559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:13.317595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:14.569246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:16.274647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:17.551267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:18.668243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:19.728443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:20.972122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:22.001369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:23.166251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:13.432290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:14.687896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:16.431228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:17.684874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:18.777950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:19.838152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:21.082828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:22.112072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:23.281941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:13.543954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:14.834503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:16.622716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:17.817558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:18.888692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:19.973749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:21.192535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:22.224772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:23.399628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:13.673644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:14.973130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:16.791300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:17.935241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:19.001353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:20.092432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:21.304234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T21:16:22.338466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-05-21T21:16:32.438441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-21T21:16:32.954105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-21T21:16:33.458709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-21T21:16:33.948399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-21T21:16:34.360297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-21T21:16:23.660964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-21T21:16:25.015302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexUnit of Measure (Per Pack)Line Item QuantityLine Item ValuePack PriceUnit PriceWeight (Kilograms)Freight Cost (USD)Line Item Insurance (USD)Managed By_Ethiopia Field OfficeManaged By_Haiti Field OfficeManaged By_PMO - USManaged By_South Africa Field OfficeFulfill Via_Direct DropFulfill Via_From RDCVendor INCO Term_CIFVendor INCO Term_CIPVendor INCO Term_DAPVendor INCO Term_DDPVendor INCO Term_DDUVendor INCO Term_EXWVendor INCO Term_FCAVendor INCO Term_N/A - From RDCShipment Mode_AirShipment Mode_Air CharterShipment Mode_OceanShipment Mode_TruckProduct Group_ACTProduct Group_ANTMProduct Group_ARVProduct Group_HRDTProduct Group_MRDTSub Classification_ACTSub Classification_AdultSub Classification_HIV testSub Classification_HIV test - AncillarySub Classification_MalariaSub Classification_PediatricFirst Line Designation_NoFirst Line Designation_Yes
003.4011972.9444396.3117353.367296-0.0304592.5649496.659730-0.2678790.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.0
115.4806396.9077558.7323051.824549-3.5065585.8805338.4165992.3456450.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.01.0
234.09434510.37098811.7547791.383791-2.6592607.5256409.6807855.3304940.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
344.09434510.54534111.7084921.163151-2.9957328.93458710.7243705.3438400.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
455.4806396.0306857.7077821.677097-3.9120236.2225768.6861631.2919840.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.01.0
564.4998104.9052758.3834333.478158-1.0216515.7930147.3317541.5129270.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.01.0
674.0943459.72118611.0159131.294727-2.8134117.2984458.7343044.1116930.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
784.0943455.6094726.2773010.667829-3.5065586.1717018.489028-0.4700040.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.0
894.7874927.93737511.6533833.716008-1.0788106.4661459.1765165.2169450.00.01.00.01.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
9104.7874927.93737511.6533833.716008-1.0788106.4661459.1765165.2169450.00.01.00.01.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0

Last rows

df_indexUnit of Measure (Per Pack)Line Item QuantityLine Item ValuePack PriceUnit PriceWeight (Kilograms)Freight Cost (USD)Line Item Insurance (USD)Managed By_Ethiopia Field OfficeManaged By_Haiti Field OfficeManaged By_PMO - USManaged By_South Africa Field OfficeFulfill Via_Direct DropFulfill Via_From RDCVendor INCO Term_CIFVendor INCO Term_CIPVendor INCO Term_DAPVendor INCO Term_DDPVendor INCO Term_DDUVendor INCO Term_EXWVendor INCO Term_FCAVendor INCO Term_N/A - From RDCShipment Mode_AirShipment Mode_Air CharterShipment Mode_OceanShipment Mode_TruckProduct Group_ACTProduct Group_ANTMProduct Group_ARVProduct Group_HRDTProduct Group_MRDTSub Classification_ACTSub Classification_AdultSub Classification_HIV testSub Classification_HIV test - AncillarySub Classification_MalariaSub Classification_PediatricFirst Line Designation_NoFirst Line Designation_Yes
8020103144.0943459.24377510.5247091.280934-2.8134118.7475119.7374923.6446660.00.01.00.00.01.00.00.00.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.00.00.00.00.01.01.00.0
8021103154.78749211.15625114.0815602.925310-1.8325819.62891910.1727517.2014170.00.01.00.00.01.00.00.00.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
8022103164.0943459.61580511.4906801.874874-2.2072757.3440738.1344684.7458880.00.01.00.00.01.00.00.00.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
8023103173.4011978.8134389.9512711.137833-2.3025858.72955910.7638763.2063980.00.01.00.00.01.00.00.00.00.00.00.00.01.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.0
8024103184.09434512.23195013.5128841.280934-2.81341110.16122610.7388196.7681020.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.00.00.00.00.01.01.00.0
8025103194.09434512.02317713.3041111.280934-2.81341110.16122610.7388196.5593180.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.00.00.00.00.01.01.00.0
8026103204.0943459.95570011.8305751.874874-2.2072758.3952529.5979755.0858050.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.01.00.00.00.00.01.00.0
8027103213.40119713.15100115.4525862.301585-1.10866311.04504011.1060818.5724460.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.01.00.00.00.00.01.00.0
8028103224.0943459.76795411.6428291.874874-2.2072757.2384978.2086024.8980640.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.01.00.00.00.00.00.01.0
8029103234.09434510.50886911.1970030.688135-3.5065589.4835689.3648704.4522520.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.00.00.00.00.01.01.00.0